the improved calibration method and retrieval models using advanced ground-based multi-frequency...

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Remote Sensing Science

November 2013, Volume 1, Issue 3, PP.27-40

The Improved Calibration Method and Retrieval

Models Using Advanced Ground-basedMulti-frequency Microwave SounderJieying He, ShengweiZhangKey Laboratory of Microwave Remote Sensing, Center for Space Science and Applied Research, Chinese Academy of Sciences,

Beijing 100190, China

Email:[email protected]

Abstract

The paper presents an improved ground-based atmospheric microwave sounder with multi-frequency channels. Compared tointernational used atmospheric microwave sounder, the merits of the instrument is described. To derive more accurate brightness

temperature from microwave sounder observation, the paper presents 4 calibration processes in different process to perfect the

calibration, including LN2 calibration, tipping curve calibration, nonlinear correction and quasi real time calibration. Since the

close relationship between cloud and integrated water vapor and liquid water content, the paper describes several cloud-judgment

models, and presents an improved cloud modal considering all the characteristics of all cloud models. Furthermore, the objective

of this study is to test ANN (Artificial Neural Network) methodology for the problems of integrated water vapor and liquid water

path derivation, using the improved ground-based atmospheric microwave sounder brightness temperatures and surface

information such as temperature, humidity, pressure and so on. Compared to the commonly used method linear regression, this

paper built a better retrieval model using ANN theory which can be high nonlinear and provide a tool to fit function to data. The

present model gave an average RMSRoot Mean Squareerror of integrated water vapor and liquid water content are less than

0.05cm and 0.5mm dependent on the actual atmospheric situation.

Keywords: Calibration; Ar tif icial Neural Network; I ntegrated Water Vapor; L iquid Water Path; Root Mean Square

1INTRODUCTION

Atmospheric integrated water vapor and liquid water content are important meteorological parameters. Water vapor

varies significantly from time to time and from space to space. The vertically integrated water vapor as well as

integrated cloud liquid water plays a key role in the study of global atmospheric circulation and evolution of clouds,

also helps one to understand the global hydrologic cycle and the earth s radiative balance. The amount of watervapor in the air varies considerably, from practically none at all up to about 4 percent by volume which includes

water vapor, ozone, nitrogen, carbon dioxide argon and others. Certainly the fact is that water vapor is the source of

all clouds and precipitation which would be enough to explain its importance.

There are two traditional sounding instruments to retrieve them: radiosonde (radar) working on the ground and

remote sensing satellite working on the high spatial orbit. The former one is bulky and costly which is perplexing to

install and operate and having lower spatial and temporal resolution. The latter one has higher spatial resolution and

wider coverage. However, due to the shelter and strong absorption of cloud, as well as atmospheric opacity for

electromagnetic wave in the millimeter-wave band, satellite instrument with limitation of remote sensing technology

has a lower vertical resolution at the bottom of troposphere.

By far, ground-based atmospheric microwave sounder has been considered the best accurate and lowest-costinstrument to retrieve integrated water vapor and liquid water content. It can be operated in a long-term unattended
mailto:[email protected]:[email protected]


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mode under almost all weather conditions with reliable results and has a low maintenance cost. Also it has a high

resolution at the bottom of lower troposphere. With the long-term development of theory and laboratory

measurements, it has been widely used in meteorological observations and forecasting, communications, geodesy

and long-baseline interferometry, satellite validation, climate and fundamental molecular physics.

The paper mainly introduces a prototype of ground-based atmospheric microwave sounder which operates in K-band

from 22 GHz to 31 GHz and V-band from 51 GHz to 59 GHz, respectively [1]. Different from the MP3000A [2] andRPG [3], the sounder adopts independent dual-band reflectors instead of sharing a dual-band reflector. The direct detect

type receiver is applied which is of smaller size, higher sensitivity, efficient data observing and lower nonlinear error

than the widely used superheterodyne receiver. The observing brightness temperatures from this prototype agree well

with simulated brightness temperatures according to the ground-based radiative transfer theory [4].

2DESCRIPTION OF GROUND-BASED MULTI-CHANNEL ATMOSPHERIC MICROWAVESOUNDER

2.1 The design of atmospheric microwave sounder

In this paper, we mainly introduce a prototype of advanced ground based atmospheric sounder with improvements.

In this prototype, different from international commonly used microwave radiometers, the receiver system adoptsdirect detect type rather than superheterodyne type which is used in almost all the microwave radiometers recently.

For the reflector-configuration, it adopts two independent reflectors in each band rather than sharing one reflector in

dual band. The description in detail has been shown in reference [1].

Microwave radiometer receiver with direct detect type mainly consists of directional coupler, RF amplifier, power

divider, band-pass filter, square law detector, integrator and video frequency amplifier. The signal accepted by

antenna is amplified and distributed into several RF channels without mixers. It uses band-pass filter to decide the

frequency for each channel. Here the bandwidths in different channels can be different. In the system, the number of

sounding-channel depends on power divider network. Different from superheterodyne type, its easy to control and

operate without mixer and frequency synthesizer. The most importance, it can detect all the channels simultaneously,

therefore it has long integrated time and high scanning efficiency. Adopting independent reflectors, we design andoptimize them independently and also make the width of antenna as small as possible. With a smaller height value

and a similar width value, the whole size and whole device expense can be decreased feasible. In this configuration

mode, we can easily release the low sidelode and design the antenna in dual-band independently without frequency

segregation. The prototype has high calibration accuracy because it use independent blackbody in each band and

need not consider the dual band requirements. Furthermore, it decreases the wave loss through radome and has high

expansibility to release multi-polarization measurements.

TABLE 1THE SPECIFICATIONS OF THE PROTOTYPE

(Ground-based multi-frequency atmospheric microwave sounder)

specification Prototype of design

Calibration resolution 1.5k

Long-term stability


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Digital control unit

Host computer

Anten

na

unit

infrared

Rainfall

GPS

220V/24V

Temperature control and humidity detain

DC/DC

CAL.

BB

Temperature control and humidity detain

N N

CAL.

BB

filterdetectorintegratorAmp Amp Amp Amp Amp Ampfilter detector integratorfeed feed

Noise diode Noise diode

devider devider

filterdetectorintegratorAmp Amp filter detector integrator Amp

FIG.1THE DIAGRAM OF GROUND-BASED MULTI-FREQUENCY ATMOSPHERIC MICROWAVE SOUNDER

2.2 Cali bration of atmospher ic microwave sounder

Calibration errors are the major source of inaccuracies in radiometric measurements. The standard calibration

procedure is to terminate the radiometer inputs with two absolute calibration targets which are assumed to be ideal

targets. So adopting proper calibration method will ensure a high sounding resolution. Also, based on the advanced

design, calibration unit has significant characteristics. It uses two calibrated methods like LN2 (22-31 GHz and

51-59GHz) calibration and sky tipping (tipping curve) calibration [5-7] (22-31GHz) to realize absolute calibration

mainly to correct the nonlinear factor and uses two-point relationship to realize quasi real-time calibration where the

nonlinear factor are determined by four-point nonlinear correction.

2.2.1 Nonlinear correction

A problematic simplification occurring in the design of calibration systems for total power receivers is the

assumption of a linear detector input power response. Even in the well defined square law regime (about -25 dBmmaximum input power) where these devices are usually operated, the detector diode is not an ideal element with

perfect linearity. Noise injection measurements performed at our prototype have clearly confirmed that a simple two

point calibration with precision absolute calibration standards (ambient temperature and liquid nitrogen cooled loads)

leads to brightness temperature errors of several K in a signal range of 0400 K. The new noise injection calibration

algorithm corrects for these nonlinearity effects.

The most common procedure to calibrate a radiometer is to terminate the receiver input with two absolute

radiometric standards and to calculate the system gain simply from the slope of a straight line given by the two

receiver responses to the different standards. The more accurate approximation to the real system response is

GTU (1)

Where U is the detector voltage, G is the receiver gain coefficient; T is the total noise power expressed in brightness

temperature and is a nonlinearity factor. T includes the system noise temperature Tsysand the scene temperature Tsc.

The nonlinearity becomes more significant for a receiver with a low system noise temperature Tsys. If Tsys is in the

order of a few hundred K, its magnitude is comparable to the scene temperature range (0 - 400 K) which occupies

about half of the detector curve. For Tsys values several times as high as the scene range, the later is only a small

fraction of the curve and can be fitted by a more linear function ( close to 1).

The problem is how to determine G, and Tsys, experimentally (three unknowns cannot be calculated from a

measurement on two standards). A solution is to generate four temperature points for calibration by additional noise

injection of temperature Tn which leads to four independent equations with four unknowns.

The initial calibration is performed with absolute standards which lead to the voltages U1 and U3. By injection ofadditional noise to the detector input signal the voltages U2 and U4 are measured. For example U2 is given by


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)(2 ncoldSYS TTTGU (2)

Where, Tcold is the radiometric temperature of the cold load (Tc = Tsys + Tcold). The evaluation of the

corresponding equations for U1, U2, U3 and U4 results in the determination of Tsys, G, and Tn. It is important to

notice that the knowledge of the equivalent noise injection temperature Tn is not needed for the calibration algorithm.

It is only assumed that Tn is constant during the measurement of U1 to U4.

Through 4 calibration points we can know 3 calibration parameters and noise injection and then decide the nonlinear

factor.

FIG.2THE COMPARISON OF CALIBRATION CURVE BETWEEN REAL AND ACTUAL SITUATION

The process only uses noise injection and in-built blackbody to realize nonlinear correction. The nonlinear error

mainly depends on nonlinear power of detect diode, so in long time, the character of nonlinearity is stable and

nonlinear factor is stable, too.

The diagram of nonlinear calibration can be shown as in Fig. 3.

FIG.3THE DIAGRAM OF MULTI-POINT NONLINEAR CORRECTION2.2.2 Sky tipping calibration

Sky tipping is a calibration procedure suitable for those frequencies where the earths atmosphere opacity is low

(high transparency) which means that the observed sky brightness temperature is influenced by the cosmic

background radiation temperature of 2.7 K. The humidity profiler channels are candidates for this calibration mode.

High opacity channels like all temperature profiler channels >53 GHz are saturated in the atmosphere and must be

calibrated by other methods such as the following method: LN2 calibration method. Sky tipping assumes a

homogeneous, stratified atmosphere without clouds or variations in the water vapor distribution. If these

requirements are fulfilled the following method is applicable: The radiometer scans the atmosphere from zenith to

around 30 in elevation and records the corresponding detector readings for each frequency. The path length for a

given elevation angle is 1/cos() times the zenith path length (often referred to as air mass), thus thecorresponding optical thickness should also be multiplied by this factor (if the atmosphere is stratified).

Tobservation Tcold Thot

Vobservation

Vcold

Vhot

R

ealc

urve

acur

alcu

rve

T

V


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The optical thickness is related to the brightness temperature by the equation:

)sec()ln()(0

Bmr

imr

(3)

Where,0BT is the cosmic background radiating temperature (approximates to 2.73k) and mrT is the mean radiating

temperature in the direction . iT is the brightness temperature of frequency channel i.

)(

0

)(

1

)(

e

dzez az

mr (4)

mrT is a function of frequency and is usually derived from radiosonde data. A sufficiently accurate method is torelate

mrT with a quadratic equation of the surface temperature measured directly by the radiometer.

The optical thickness as a function of air mass is a straight line, which can be extrapolated to zero air mass. The

detector reading Usys at this point corresponds to a radiometric temperature which equals to the system noise

temperature plus 2.7 K: Usys =G*(Tsys + 2.7 K). The proportionality factor (gain factor) G can be calculated when a

second detector voltage is measured with the radiometer pointing to the ambient target with known radiometric

temperature Ta. The sky tipping calibrates the system noise temperature and the gain factor for each frequency

without using a liquid nitrogen cooled target. The disadvantage of this method is that the assumption of a stratifiedatmosphere is often questionable even under clear sky conditions due to invisible inhomogeneous water vapor

distributions (e.g. often observed close to coast lines). The built-in sky tipping algorithm investigates certain user

selectable quality criteria to detect those atmospheric conditions that do not fulfill the calibration requirements.

The tip curve calibration is considered to be the most accurate absolute calibration method. The brightness

temperatures acquired in the elevation scan are close to the scene temperatures measured during zenith observations.

2.2.3 LN2 calibration

One of absolute calibration standards is the liquid nitrogen cooled target that is attached externally to the radiometer

box. This standard - together with the internal ambient load - is used for the absolute calibration procedure. Different

from TIP calibration method, LN2 calibration can be used both in k-band and v-band channels. The noise diodes in

profiling radiometers are used as the secondary standard to measure system gain in each channel for eachobservation. When enabled, they add a calibrated increase to the brightness temperatures. When the value of Tnd is

not known, it can be determined by observing two targets of known temperature. In the fully automated method used

by radiometric, the built in ambient black body target provides one target of known temperature for the calibration,

and an external cryogenic target, filed with LN2, provides the second. The ambient target physical temperature

(TkBB) can be measured by the instrument.

2.2.4 Quasi real-time calibration

The above three calibration methods are operated before the instrument can be used normally, however, the aim of

quasi real-time calibration is to calibrate the channel gain and noise fluctuation of receiver and to ensure the probe

accuracy not be effected by the system noise fluctuations and noise drift.

The quasi real-time calibration period depends on short-term stability and can be 10-20 minutes withtemperature-controller in relative stable environment. Internal calibration unit consists of noise injection block and

in-built calibration blackbody, and both provide stable reference. Noise injection block consists of noise source

(noise diode) which provides noise to be calibrated, switch which turn on and off the noise signal and directional

coupler which is used for noise injection. We can used microwave switch or supply power to turn on and off the

noise signal. Directional coupler is used for feeding into noise signal which temperature is 50-80k.

In-built calibration blackbody provides standard brightness temperature (~ambient temperature). To ensure the

stability, there are many pt-resistances to measure the temperature gradients. In order to reduce the gradient, it is

optimal to use foam material which has performance of insulation as blackbody calibration layer and DC mini-fan to

drive the airflow.

In the atmospheric sounding process of ground-based atmospheric microwave sounder, by injecting noise andobserving the in-built blackbody periodically, we can determine the local noise and receiver system gain; it means


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that we can determine the calibration equation which will be used to the retrievals of atmospheric brightness

temperatures. In quasi real-time calibration, we use two-point calibration method using nonlinear correction factor,

given the observing voltages, the observing brightness temperatures can be derived using the nonlinear relationship

between in-built blackbody and blackbody with an noise injected by noise injection module.

3SOUNDING PRINCIPLES

The integrated water vapor and liquid water content can be achieved by two-channel radiometers was demonstrated

more than two decades ago [8-9]. Typically, the atmospheric brightness temperature is measured at one frequency on

the wing of water vapor line at 23.834GHz and at a second frequency in the window region around 30GHz. The first

frequency is chose so that the water vapor absorption coefficient is nearly independent of altitude. Because the

emission of cloud liquid water increases with the frequency squared, the signal at the second frequency is dominated

by liquid water contribution. From measurements at both frequencies integrated water vapor and liquid water content

can be retrieved simultaneously [10, 11].

Recently, it has been suggested that cloud liquid water content retrievals can be improved by adding a

temperature-dependent frequency around 50 GHz [Bosisio and Mallet, 1998] or an additional frequency sensitive to

cloud liquid water at 85GHz [Bobak and Ruf, 2000]. Within this study, we will be compare the cloud liquid water

content retrieval accuracies obtained with different frequency combination obtained from ground-based

multi-frequency atmospheric microwave sounder. The chosen frequencies in this paper are 23.84GHz, 30GHz and

51.25GHz.

MPM 89 model is a broadband model for complex refractivity which is presented to predict propagation effects of

loss and delay for the neural atmosphere at frequencies up to 1000GHz. The contributions are accounted from dry air,

water vapor, suspended water droplets (haze, fog, cloud), and rain are addressed. For clear air, the local line base (44

oxygen lines and 30 water vapor lines) is complemented by an empirical water vapor continuum. Input variables are

barometric pressure, temperature, relative humidity, suspended water droplet concentration and rainfall rate.

Cruz et al. simplified MPM87 model [12] for water vapor absorption together with the improved oxygen absorption

model by Rosenkranz [13]. Refinements to the water vapor absorption model are accomplished by the addition ofthree adjustable parameters, LC , WC and cC , which account for scaling of the line strength, line width and

continuum term, respectively. The oxygen absorption model is refined with the addition of the adjustable scaling

factorXC

[14].

According to the comparison between MPM 89 and CRUZ model, for the chosen frequencies 23.834GHz, 30GHz

and 51.248GHz, we use CRUZ model for the first and MPM 89 model for the left two.

Figure 4 shows the atmospheric attenuation and absorption coefficients from 1 to 200 GHz, including water line

spectrum absorption, oxygen line spectrum absorption, dry-air absorption and water continuum absorption.

0 20 40 60 80 100 120 140 160 180 20010

-6

10-5

10-4

10-3

10-2

10-1

100

101

102

frequency (GHz)

absorption

dB/km

US standard atmosphere, 1976

absorption-waterlines

absorption-continum

absorption-dryair

absorption-oxygen

FIG.4THE ATMOSPHERIC ABSORPTION MODEL FROM 0-200GGHZ


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Fig.5-6 shows the weighting function distributions for all channels of advanced multi-channel advanced

ground-based microwave radiometer calculated for a U.S. standard arctic at nadir using an atmospheric absorption

model (MPM model). The weighting functions indicate the radiative contribution of each atmospheric layer to the

measured radiance. For a given atmosphere and frequency, the peak altitude in the weighting function increases with

increasing zenith angle. This is due to increasing optical path length between the zenith and the earth when the

instruments scan from zenith to higher angles. In window channels the weighting function peaks have their

maximum closer to the surface. Most of the radiance measured by these window channels comes from the surface

and the boundary layer and these channels can be used to derive total precipitable water, precipitable rate or cloud

liquid water over ocean [15-16].

0.5 1 1.5 2 2.5 3 3.50

1

2

3

4

5

6

7

8

9

10water vapor weighting functions

weight values

h

eight(km)

22.235

23.035

23.835

26.235

30.00

0 0.5 1 1.5 2 2.50

1

2

3

4

5

6

7

8

9

10temperature weighting functions

weight values

h

eight(km)

51.25

52.28

53.85

54.94

56.66

57.29

58.80

FIG.5THE WEIGHTING FUNCTIONS OF GROUND-BASED MULTI-FREQUENCY MICROWAVE SOUNDER

In Fig.6, the weighting functions of 12 channels are calculated for a U.S standard arctic atmosphere at zenith,

assuming a surface temperature of 290k, surface pressure of 1013 mbar and surface relative humidity of 70%) (a:

20-30GHz b: 50-60GHz)

FIG.6THE WEIGHTING FUNCTIONS OF GROUND-BASED MULTI-FREQUENCY MICROWAVE SOUNDER FOR DIFFERENT ANGLES

In Fig.6, the weighting functions are calculated for a U.S standard arctic atmosphere at zenith, assuming a surface

temperature of 290k, surface pressure of 1013 mbar and surface relative humidity of 70%. The weighting functions

in different angles at 23.84GHz and 51.25 GHz, respectively.

4CLOUD MODEL DETERMINATION

A common approach is to place clouds in a radiosonde profile, where the relative humidity exceeds a threshold of

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

1

2

3

4

5

6

7

8

9

10

normalized weighting functions

altitude(km)

54.94GHz

0 degree

20 degree

30 degree

40 degree

50 degree

60 degree

70 degree

80 degree

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

1

2

3

4

5

6

7

8

9

1022.235GHz

normalized weighting functions

altitude(km)

0 degree

20 degree

30 degree

40 degree

50 degree60 degree

70 degree

80 degree


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95%. Within the development of retrieval algorithms brightness temperatures based on these cloud liquid water

profiles are simulated to obtain relations between the brightness temperatures and cloud liquid water path. Generally

radiative transfer is drop size distribution dependent in the microwave region, although the drop size distribution will

only be significant at higher frequencies like 90GHz or in rainy calculations in this study, therefore the scattering is

calculated according to the MIE theory for all clouds.

4.1 Salonen cloud l iquid water content model (salonen, 1991)

The following model is illustrated for a standard atmospheric profile in Fig.7 and 8.

The critical humidity Function[17]

, cU , is calculated at each altitude with the formula

)]5.0(1)[1(1 cU (5)

Where =1, 3 ,

is the ratio of the atmospheric pressure at each height to surface pressure.

Cloud is assumed to occur at each altitude where the relative humidity is greater than cU . This method is illustrated

using a simulation of a standard atmospheric profile.

FIG.7THE CRITICAL HUMIDITY FUNCTION CALCULATED USING USSTANDARD ATMOSPHERE,1976.

FIG.8THE CLOUD DETERMINATION USING STANDARD CRITICAL HUMIDITY FUNCTION.

4.2 Three cloud models proposed by Ulr ich et al.

Ulrich Lohnert et al. presented 3 models to estimate cloud drop size distribution, like TH mode, CE method and

Dynamic cloud mode in 2003 [18]. TH method is to diagnose cloud liquid water from radiosonde measurements use

a threshold on relative humidity (RH). In Ulrichs study, the threshold is set by 90% or 95% thresholds, cloud layers

are taken to exist in a profile when RH exceeds the corresponding value. CE method is an alternative approach forderiving cloud boundaries from radiosonde ascents which is a gradient method proposed by Chernykh and Eskride in

1996. A cloud is modeled into layers when it satisfies the determined conditions. Dynamic cloud model calculates

the cloud liquid water in 40 logarithmic radius classes every 250m from ground to 10km height. This convective

model was initialized with the modeling of TH mode and CE method. The disadvantage of this model is that it is

always generates clouds, and hence clear sky cases are not well represented.

4.3 The improved cloud model

In this paper, the authors combined Salonen model and TH method from Ulrich Lohnert model to detect the real

conditions happened in the train datasets and validation datasets based on the radiosonde observations. The improved

cloud model can be described as follows:

According to the humidity profiles from radiosonde datasets, first, we use equation of Salonen model to derive the

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

1

2

3

4

5

6

7

8

9

10

relative humidity

altitude(km)

critical humidity function

RH profile

0.7 0.75 0.8 0.85 0.9 0.95 10

1

2

3

4

5

6

7

8

9

10

relative humidity

altitude(km)

US standard atmosphere ,1976

critical cloud function


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critical humidity function at each discrete altitude and judge the height value where relative humidity value is larger

than critical humidity function value, then record the relative humidity value of cloud base and cloud top and judge

the relationship among them and humidity threshold from TH model. Second, using TH model and Salonen model to

derive cloud distribution, respectively. Where cloud occurs, the cloud liquid water content, w (g/m3), as a function of

temperature t (degree), and height from cloud base, is given by

)())(1(0 tPhhctww w

r

c (6)

Whrer, a=1.4 is the parameter for height dependence. c=0.041/oC is the parameter for temperature

dependence.0

w =0.14 g/m3is the liquid water content, if rc hh =1.5km at 0oC. (i.e. at cloud base), )(tPw is the

liquid water fraction, approximated by

)(tPw =1 if 0oC


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vapk , liqk are the frequency-dependent path averaged mass absorption coefficients.Integrated water vapor content dsV v 0 2/ mg (13)

0

VVmm (cm) (14)

Integrated Liquid water content dsLr

r l

2

1

2

/ mg (15)

0

LLmm (mm) (16)

Where,0

denotes the water vapor densityin the unit of 3/ mg ,

1rand

2r are heights of cloud base and cloud top,

maybe there is more than one cloud layer, so the integrated Liquid water content can be the sum of several integrants.

v and l are the water vapor density and cloud liquid water density in atmosphere at height z, the unit of

parameter ds is m, and V and L are the integrated content of water vapor and liquid water in unit valume of the

cylinder.

6RETRIEVAL METHOD

6.1 Regression method

If the dry contribution is determined separately, it can be subtracted such that

d r * (17)

Andthe three equations can be solved for the estimations of V and L:3322110

aaaaV (18)

3322110 bbbbL (19)

Where, 1 , 2 and 3 are the opacity in three channel, 0a , 1a , 2a , 3a , 0b , 1b , 2b and 3b are the retrievalcoefficients. The subscripts 1, 2 and 3 refer to the vapor, liquid water and oxygen-sensitive frequencies 22.235GHz,

30GHz and 51.25GHz. At each frequency, the opacity is calculated from the measured sky brightness Tsky derived

from equation 17.

The water vapor coefficients,0

a ,1

a ,2

a ,3

a , exhibit a weak dependence on surface pressure and surface temperature

and humidity. The linear relationship between opacity (brightness temperature) and water vapor density profile is

evident. So using the above three equations, we can derive the water vapor density profiles using linear regression

method.

Retrieval of the cloud liquid water content is complicated by the fact that liqk decreases exponentially as liquid

water temperature increases, and thus depends strongly on the height and thickness of the cloud. Consequently, the

liquid water retrieval coefficients,0

b ,1b ,

2b and

3b , also exist a strong dependence on cloud water temperature.

Also, the nonlinear regression method can be expressed as follows:

2

24

2

1322110 aaaaaV 080706215 UaTaPaa (20)

2

24

2

1322110 bbbbbL 080706215 UbTbPbb (21)

In the above two equations, the surface temperature, humidity and pressure are added, also the square terms and

cross-terms are added.

In this paper, here we use brightness temperature in chose channels instead of atmospheric opacity to construct linear

regression model and derive the regression coefficients.

6.2. Ar tif icial neural network


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)(049329.0)(045409.066426.021 vTvTh APAPv

)(046047.0)(01074.040282.0 21 vTvTh APAPL

Using the other channel at 51.248GHz, the coefficients are:

)(035278.0)(00131.0)(012167.0459.3 321 vTvTvTh APAPAPL

)(04462.0)(11658.0)(10276.01865.4 321 vTvTvTh APAPAPV

The following retrievals are derived using three channels, which are 23.835GHz, 30GHz and 51.25GHz. From the

retrievals it can been seen that using liner regression method can have accurate integrated water vapor content

compared to radiosonde datasets, however, using ANN method the retrievals are more agreement with the

radiosonde content, like shows in Fig.10. and compared the retrievals there is a good agreement between from

regression method and from MP3000A. Based on same method, this paper compared cloud liquid water content

among regression method, ANN method and from MP3000A. Also, Fig.13 shows that compared to regression

method, ANN method can get the retrievals more agreement with retrievals form MP3000A rather than regression

method. Because there is no accurate cloud liquid water content observed by radiosonde or other instruments, so in

this paper, MP3000A liquid water retrievals are considered as standard. Therefore, the retrieval root mean square

errors between MP3000A and ANN model is only a relative value. Therefore, further comparison need to do in

future.

0 50 100 150 200 2500

1

2

3

4

5

6

7

data 0800 (2008.5-2008.12)

IWV(cm)

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

datasets(from 2008.5 to 2008.12)

int

egratedwatervapor(cm)

RAOB

linear regression

ANN

FIG.9THE ACTUAL ATMOSPHERIC INTEGRATED WATER VAPOR CONTENT FROM 2008.5TO 2008.12

FIG.10THE COMPARISON OF INTEGRATED WATER VAPOR CONTENT AMONG LINEAR REGRESSION,ANNAND MP3000

INSTRUMENT IN WINTER FROM 2008.10TO 2008.12.

0 5 10 15 20 250

1

2

3

4

5

6

24 dataset (2008.5-2008.12:0800)

integratedwatervaporcontent

cm

linear regression

MP3000

FIG.11THE COMPARISON OF INTEGRATED WATER VAPOR CONTENT RETRIEVALS BETWEEN LINEAR REGRESSION METHOD AND

MP3000INSTRUMENT IN WINTER FROM 2008.5TO 2008.12.


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0 50 100 150 200 2500

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

data 0800 (2008.5-2008.12)

CLW(m

m)

0 2 4 6 8 10 1 2 1 4 16 18 20-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

datasets (from 2008.5 to 2008.12)

integratedliquidwater

vaporcontent(mm)

MP3000

linear regression

ANN

FIG.12THE LIQUID WATER CONTENT FROM 2008.5TO 2008.12

FIG.13THE COMPARISON OF LIQUID WATER CONTENT AMONG LINEAR REGRESSION METHOD,ANNMETHOD AND MP3000

INSTRUMENT FROM 2008.5TO 2008.12.

9CONCLUSION AND ANALYSIS

The performance of the advanced multi-channel ground-based microwave radiometer has the facility to provide

approximated time series measurements of integrated water vapor content and liquid water content. The instrument

has proven to be durable and reliable in continuous field observation. Also, this paper demonstrates that nonlinear

correction process is also very important to get brightness temperatures which are more agreement with the actual

theory values. Tipping and liquid nitrogen calibration in 20-30 and 50-60GHz are proper and can ensure that system

resolution and noise are in the acceptable level. Using regression method and artificial neural network, the retrievals

are reflect the various of integrated water vapor and liquid water content in time series. And compared to regression

method, ANN retrievals are more agreement with the radiosonde or MP3000A with smaller root mean square error

(the retrieval root mean square error of integrated water vapor and liquid water content are 0.0475 cm and 0.5mm,respectively).

ACKNOWLEDGEMENTS

The work presented in this paper was sponsored by the China Meteorological Administration nonprofit sector

(meteorology) special research and grant Nos. GYHY200906035 and grant Nos. 863 High-Techs 2007AA120701.

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AUTHORS

Jieying HE Research Assistant of CAS key laboratory of microwave remote sensing, National space sciencecenter, Chinese Academy of Sciences (CAS), and get Doctor degree of philosophy (CAS key laboratory of

microwave remote sensing, National space science center, Chinese Academy of Sciences, Beijing) in 2012, now

the researching fields are microwave Remote Sensing, Atmospheric radiative transfer theory, Ground-based

microwave radiometer calibration theory and Temperature and humidity retrievals based on Satellite-based and

ground-based microwave radiometer.

the improved calibration method and retrieval models using advanced ground-based multi-frequency...

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